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sklearn.linear_model.orthogonal_mp_gram

sklearn.linear_model.orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True, return_path=False, return_n_iter=False) [source]

Gram Orthogonal Matching Pursuit (OMP)

Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y.

Read more in the User Guide.

Parameters:

Parameters:	`Gram : array, shape (n_features, n_features)` Gram matrix of the input data: X.T * X `Xy : array, shape (n_features,) or (n_features, n_targets)` Input targets multiplied by X: X.T * y `n_nonzero_coefs : int` Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. `tol : float` Maximum norm of the residual. If not None, overrides n_nonzero_coefs. `norms_squared : array-like, shape (n_targets,)` Squared L2 norms of the lines of y. Required if tol is not None. `copy_Gram : bool, optional` Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway. `copy_Xy : bool, optional` Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. `return_path : bool, optional. Default: False` Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. `return_n_iter : bool, optional default False` Whether or not to return the number of iterations.
Returns:	`coef : array, shape (n_features,) or (n_features, n_targets)` Coefficients of the OMP solution. If `return_path=True`, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis yields coefficients in increasing order of active features. `n_iters : array-like or int` Number of active features across every target. Returned only if `return_n_iter` is set to True.

Gram : array, shape (n_features, n_features): Gram matrix of the input data: X.T * X
Xy : array, shape (n_features,) or (n_features, n_targets): Input targets multiplied by X: X.T * y
n_nonzero_coefs : int: Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.
tol : float: Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
norms_squared : array-like, shape (n_targets,): Squared L2 norms of the lines of y. Required if tol is not None.
copy_Gram : bool, optional: Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway.
copy_Xy : bool, optional: Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten.
return_path : bool, optional. Default: False: Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation.
return_n_iter : bool, optional default False: Whether or not to return the number of iterations.

Returns:

coef : array, shape (n_features,) or (n_features, n_targets): Coefficients of the OMP solution. If return_path=True, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis yields coefficients in increasing order of active features.
n_iters : array-like or int: Number of active features across every target. Returned only if return_n_iter is set to True.

Notes

Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)

This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

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http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html